ARGO builds, operates and maintains pioneering data infrastructure to transform how basic public services like street quality, water reliability, and education volunteering are delivered.

In one to three sentences, please describe your project proposal.

SQUID is a low-cost device that uses open hardware to collect images and ride quality data across the entire city street grid to enable and empower the city or county of LA to offer efficient and equitable response around street maintenance.

Central LA, County of Los Angeles (please select only if your project has a countywide benefit), City of Los Angeles (please select only if your project has a citywide benefit), LAUSD

Describe in greater detail how your proposal will make LA the best place to CONNECT?

It is said that street maintenance is the most visible indicator of local government performance. LA's street network contains over 6,500 center lane miles of streets which makes maintenance a daunting task. LA has already begun using digital methods to survey the city for trash and prioritize cleanup efforts. SQUID is a logical conclusion of this approach by integrating low-cost sensors , the cloud and computer vision to empower the LA city and county to be more proactive and enter an anticipatory paradigm for street maintenance.

The results, if adopted at scale and over a period of time, we believe will bring about significant efficiencies through better allocation of resources and more importantly an equitable response to street maintenance which is unprecedented.

The basic concept is to collect a 'complete' data about all, not just a few streets. SQUID does this by leveraging low-cost sensors and cloud technology to allow an inexpensive method to collect large data.

A typical lifecycle of using the SQUID device to collect data and improve decision making around LA's street maintenance involves mounting the SQUID to city vehicles and passively collecting data about street quality.

Data is collected by automatically taking a picture of the street every second and combining it with a ride quality score. A fleet of 10 vehicles driving 6 hours a day could theoretically cover LA's entire street grid in about a week. We have already prototyped this approach with New York City and Syracuse.

Once the data is collected, it is cleaned using LA's street grid GIS file and uploaded to a user interface, we call SQUID annotator. The purpose of the annotator is to allow qualified street inspectors to 'virtually' inspect every street in Los Angeles using the image data. Individual street defects like cracking, hummocks, potholes or poor lane markings can be annotated on the interface.

Once all streets images are adequately annotated, a robust response model can be developed quickly to determine where street maintenance resources need to be deployed.

We are also testing automated methods of defect detection using computer vision

The most impactful part of this approach is when data collection is done repeatedly. Longitudinal data for ALL of LA's streets unlocks an anticipatory response paradigm. Assuming LA commits to perform at least 4 surveys in a year - the resulting data would be unprecedented in terms of fidelity and scale. The degradation rates of individual streets could be 'seen' through the data prompting a more proactive response. Repaving decisions could involve using higher grade materials for some highly trafficked streets. The complete nature of this dataset could also be used as a powerful proxy for other city phenomena such as traffic indicators.

While focussing on street quality, SQUID can be repurposed to inspect other facets of the urban environment and integrate several operational silos around street imagery.

Please explain how you will define and measure success for your project.

Success in broad terms means empowering the city of LA to evolve from using a subjective index of using letter grades to measure street maintenance to using more robust data and a literal ground truth through imagery for efficient and equitable response.

Establishing a protocol for longitudinal data collection could also unlock a new paradigm of preventive street maintenance in the future

More concretely though, success can be first measured at the data collection phase in achieving near complete street-image coverage of LA's entire street grid. Beyond this, the prioritization of street maintenance using SQUID data could lead to significant cost-savings, over time and can be measured.

Taken to its logical conclusion, another vector of success is aiming to integrate various operational silos around street-level imagery and mobile data collection i.e. cleanliness assessments, asset management, pavement maintenance and even air quality (* if SQUID is fitted with an air quality sensor). We are careful though not to over-promise and under-deliver.

How can the LA2050 community and other stakeholders help your proposal succeed?